To model the prediction of stock return, traditional methods include AutoRegressive [1]}, Moving Average [2]} and Kalman Filters [3]}. Nowadays, the deep neural networks (DNNs) have shown great potential, which significantly outperforms the conventional approaches [4]}, [5]}, [6]}. Though promising results are obtained, the interferential features existed in the noised raw stock data have seriously restricted the model performance [7]}, [8]}.
In this work, we focus on the market and excess return predictions, and we find the factors related to these two returns are highly entangled/correlated in the noised raw data, which makes the market factors be interferential features for the excess return prediction [9]}. Similarly, the excess factors also hurt the performance of market return prediction. Besides, other noisy interferential factors also disrupt the two return predictions. Therefore, it is critical to remove the interferential features so that both return predictions can be benefited.
